126 lines
4.4 KiB
Python
126 lines
4.4 KiB
Python
import numpy
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import pytest
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from sklearn.linear_model import LogisticRegression
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.svm import LinearSVC
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import quapy as qp
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from quapy.method import AGGREGATIVE_METHODS, NON_AGGREGATIVE_METHODS, EXPLICIT_LOSS_MINIMIZATION_METHODS
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from quapy.method.meta import Ensemble
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datasets = [pytest.param(qp.datasets.fetch_twitter('hcr'), id='hcr'),
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pytest.param(qp.datasets.fetch_UCIDataset('ionosphere'), id='ionosphere')]
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learners = [LogisticRegression, MultinomialNB, LinearSVC]
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@pytest.mark.parametrize('dataset', datasets)
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@pytest.mark.parametrize('aggregative_method', AGGREGATIVE_METHODS.difference(EXPLICIT_LOSS_MINIMIZATION_METHODS))
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@pytest.mark.parametrize('learner', learners)
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def test_aggregative_methods(dataset, aggregative_method, learner):
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model = aggregative_method(learner())
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if model.binary and not dataset.binary:
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print(f'skipping the test of binary model {model} on non-binary dataset {dataset}')
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return
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model.fit(dataset.training)
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estim_prevalences = model.quantify(dataset.test.instances)
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true_prevalences = dataset.test.prevalence()
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error = qp.error.mae(true_prevalences, estim_prevalences)
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assert type(error) == numpy.float64
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@pytest.mark.parametrize('dataset', datasets)
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@pytest.mark.parametrize('elm_method', EXPLICIT_LOSS_MINIMIZATION_METHODS)
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def test_elm_methods(dataset, elm_method):
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try:
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model = elm_method()
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except AssertionError as ae:
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if ae.args[0].find('does not seem to point to a valid path') > 0:
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print('Missing SVMperf binary program, skipping test')
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return
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if model.binary and not dataset.binary:
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print(f'skipping the test of binary model {model} on non-binary dataset {dataset}')
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return
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model.fit(dataset.training)
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estim_prevalences = model.quantify(dataset.test.instances)
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true_prevalences = dataset.test.prevalence()
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error = qp.error.mae(true_prevalences, estim_prevalences)
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assert type(error) == numpy.float64
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@pytest.mark.parametrize('dataset', datasets)
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@pytest.mark.parametrize('non_aggregative_method', NON_AGGREGATIVE_METHODS)
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def test_non_aggregative_methods(dataset, non_aggregative_method):
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model = non_aggregative_method()
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if model.binary and not dataset.binary:
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print(f'skipping the test of binary model {model} on non-binary dataset {dataset}')
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return
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model.fit(dataset.training)
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estim_prevalences = model.quantify(dataset.test.instances)
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true_prevalences = dataset.test.prevalence()
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error = qp.error.mae(true_prevalences, estim_prevalences)
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assert type(error) == numpy.float64
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@pytest.mark.parametrize('base_method', AGGREGATIVE_METHODS.difference(EXPLICIT_LOSS_MINIMIZATION_METHODS))
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@pytest.mark.parametrize('learner', learners)
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@pytest.mark.parametrize('dataset', datasets)
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@pytest.mark.parametrize('policy', Ensemble.VALID_POLICIES)
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def test_ensemble_method(base_method, learner, dataset, policy):
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qp.environ['SAMPLE_SIZE'] = len(dataset.training)
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model = Ensemble(quantifier=base_method(learner()), size=5, policy=policy, n_jobs=-1)
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if model.binary and not dataset.binary:
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print(f'skipping the test of binary model {model} on non-binary dataset {dataset}')
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return
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model.fit(dataset.training)
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estim_prevalences = model.quantify(dataset.test.instances)
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true_prevalences = dataset.test.prevalence()
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error = qp.error.mae(true_prevalences, estim_prevalences)
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assert type(error) == numpy.float64
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def test_quanet_method():
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dataset = qp.datasets.fetch_reviews('kindle', pickle=True)
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qp.data.preprocessing.index(dataset, min_df=5, inplace=True)
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from quapy.classification.neural import CNNnet
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cnn = CNNnet(dataset.vocabulary_size, dataset.n_classes)
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from quapy.classification.neural import NeuralClassifierTrainer
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learner = NeuralClassifierTrainer(cnn, device='cuda')
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from quapy.method.meta import QuaNet
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model = QuaNet(learner, sample_size=len(dataset.training), device='cuda')
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if model.binary and not dataset.binary:
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print(f'skipping the test of binary model {model} on non-binary dataset {dataset}')
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return
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model.fit(dataset.training)
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estim_prevalences = model.quantify(dataset.test.instances)
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true_prevalences = dataset.test.prevalence()
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error = qp.error.mae(true_prevalences, estim_prevalences)
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assert type(error) == numpy.float64
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